In the field of computer vision, the task of noise estimation is very important for various applications, including image denoising, image segmentation, and image inpainting. Low-light, uncompressed noisy image datasets are commonly used to study the performance of various image denoising algorithms on artificial Gaussian noise. However, it is known that real cameras used to provide the images for the image datasets studied do not produce artificial Gaussian noise.
Working with artificial Gaussian noise allows for the simplicity of experimenting by using a single constant noise value defined by the experimenter and avoids the difficulty inherent in estimating the noise level function in real low-light noisy images. However, what is currently needed in the art is a model to better estimate various tuning parameters for use in image processing algorithms. In particular an improved system and method are needed for estimating a tuning parameter for use in a denoising algorithm, which will lead to better denoising results.
Accordingly, what is needed in the art is a system and method for automatically estimating tuning parameters for use in image processing algorithms.